The present invention relates to an adaptive knowledge decision system in a knowledge base system, and in particular, to an adaptive knowledge inference method and a knowledge processing system therefor. The invention is suitable for estimating knowledge that can be adapted to obtain an inference. The estimation is particularly applicable to a case where there exists rules referencing primary events not provided with a determining means or where there exist various knowledge items including events and rules respectively associated with an object, the knowledge items overlap with each other and change continuously with respect to time.
Fuzzy inference, fuzzy subsets of events to be measured, and applications of fuzzy algorithms have been described in detail in articles such as L. A. Zadeh, "Outline of a New Approach to the Analysis of Complex Systems and Decision Processes", IEEE Trans., Vol. SMC-3, No. 1, January 1973, pp. 28-44 and E. H. Mamdani et al., "Application of fuzzy algorithms for control of simple dynamic plant", PROC, IEE, Vol. 121, No. 12, December 1974, pp. 1585-1588.
In the conventional knowledge base system, vague primary appear for which no determining means such as measuring means are provided. In a case where there exists a rule which refers to these primary events, and since an inference operation cannot be achieved by use of knowledge thereabout, the execution of the knowledge is avoided or skipped or omitted so as to enable the inference to be effected. Alternatively, there is configured a different knowledge for estimation so as to predict the primary events by executing the knowledge, thereby enabling the events to be adaptive to the inference.
Also in a case where there exist a plurality of various knowledge items associated with an object and it is necessary to determine appropriate knowledge items to be adapted or employed for the inference, it has been a common practice to classify the knowledge items in advance as described above, a different knowledge judges the appropriate and, knowledge items for adaptation to the inference, thereby determining or forecasting the adaptive knowledge through the inference. As an example in which the knowledge cannot be clearly classified and hence the adaptation is effected by allowing overlapping of knowledge, reference may be made to an article: P. Jackson, "Introduction to Expert System", Addison Wesley, 1986, Chapter 6, MYCIN: Medical Diagnosis using Production Rules, pp. 93-106. In the MYCIN, for individual events and rules respectively described as knowledge, there is introduced a grade or magnitude representing uncertainty ranging from -1.0 to 1.0, thereby enabling the knowledge to be adapted to the inference. First, due to the grade, there are allowed overlapped portions between the respective events and rules each having an arbitrary uncertainty. Next, for the inference, the inference engine or mechanism refers the grade of each event and each rule such that events associated with the same conclusion are combined with each other through an arithmetic rule effected with respect to the grade. In consequence, it is possible also after the inference that the events and rules are adapted to the inference in the state where the overlapping exists between the events and rules.
In the prior art technology, due to existence of vague primary events for which no determining means are provided, the execution of an inference with knowledge thereabout is avoided or is omitted as described above, which means that a portion of knowledge is missing and hence which leads to a problem that the application range of the knowledge is restricted, namely, the knowledge becomes incomplete. Alternatively, in a case where the different knowledge is introduced, it is indispensable to construct such new knowledge; furthermore, considerations are required to be given to the adequacy of the respective knowledge items and the completeness as well as consistency of the knowledge as the whole, which imposes a heavy load on the operator's work. If the knowledge thus introduced is heterogeneous for the existing knowledge, for example, a mathematical model for a rule, there arises a problem that the integrity of the knowledge is lost and hence this operation is not appropriate. In addition, in a case where due to existence of various knowledge items associated with events and rules of objects, the knowledge items are classified so as to predict adaptive knowledge based on judging knowledge thus obtained, there also occurs a problem similar to the problem above, for example, there is required the different knowledge for the judgment. Furthermore, when the knowledge of the object cannot be clearly classified, the adaptation of the knowledge is impossible. Particularly, in a case where the characteristics of the object are not sufficiently determined, unclearness and uncertainty appear in the overall knowledge, and hence the clear classification thereof cannot be conducted such that adaptive knowledge items overlap with each other in many cases; moreover, the estimated knowledge is attended with vagueness in an ordinary case. According to the method of MYCIN described above, although there exists the overlapping as a result of the introduction of the grade representing the uncertainty, the grade related to the rule is a static value beforehand assigned thereto and hence cannot be dynamically changed. For example, in a case where a state of an object undergoes a transition with respect to time in a continuous fashion, there may exist a plurality of rules to be adapted thereto and values of the grade applied to the rules may dynamically and continuously change. However, in this case, at an intermediate point of an inference of the grade itself, there is required an inference effected based on an external input and on different knowledge, namely, it is impossible to achieve the consecutive acquisition and update for the inference based on the own knowledge. In addition, the conventional methods including that of MYCIN are provided with an inference engine or mechanism, and modification as well as reconstruction thereof to configure a desired method are attended with difficulties. Namely, it is required for the user to search for and to select a knowledge base system meeting the demand or to construct such a knowledge base, which considerably increases the amount of work required therefor.